AIMC Topic: Machine Learning

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Eleven quick tips for data cleaning and feature engineering.

PLoS computational biology
Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery m...

Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme.

Computational intelligence and neuroscience
Physiological status plays an important role in clinical diagnosis. However, the temporal physiological data change dynamically with time, and the amount of data is large; furthermore, obtaining a complete history of data has become difficult. We pro...

Heterogeneous ensemble learning for enhanced crash forecasts - A frequentist and machine learning based stacking framework.

Journal of safety research
INTRODUCTION: This study aims to increase the prediction accuracy of crash frequency on roadway segments that can forecast future safety on roadway facilities. A variety of statistical and machine learning (ML) methods are used to model crash frequen...

Early stopping by correlating online indicators in neural networks.

Neural networks : the official journal of the International Neural Network Society
In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus ...

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems.

Bioresource technology
Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available inform...

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

Cell biochemistry and function
The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronav...

Multi-Task Learning Model for Kazakh Query Understanding.

Sensors (Basel, Switzerland)
Query understanding (QU) plays a vital role in natural language processing, particularly in regard to question answering and dialogue systems. QU finds the named entity and query intent in users' questions. Traditional pipeline approaches manage the ...

Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks.

Scientific reports
The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense ...

Prediction of biphasic separation in CO absorption using a molecular surface information-based machine learning model.

Environmental science. Processes & impacts
Carbon dioxide capture technologies have become a focus to overcome global warming. Biphasic absorbents are one of the promising approaches for energy-saving CO capture processes. These biphasic absorbents are mainly composed of a mixed solvent compo...

Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach.

PloS one
We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the dr...